Survival analysis of time-to-event data in respiratory health research studies
Jessica Kasza, Darren Wraith, Karen E. Lamb, Rory Wolfe
Abstract
Jessica Kasza, Darren Wraith, Karen E. Lamb, Rory Wolfe
Abstract
\n\t\t\t\t\tThis article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.\n\t\t\t\t
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\n\t\t\t\t\tThis article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.\n\t\t\t\t
Key concepts: Proportional hazards model, Accelerated failure time model, Survival analysis, Event (particle physics), Statistics, Event data, Medicine, Econometrics